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Severity of complications is associated with impaired health‐related quality of life in people with type 1 diabetes
No full textInternational audienceAims Health‐related quality of life (HRQoL) assessment is increasingly integrated into type 1 diabetes (T1D) monitoring to promote a holistic approach. To investigate HRQoL in adults with T1D and to assess the impact of the severity of complications on HRQoL. Materials and Methods This is a cross‐sectional analysis of baseline characteristics of adults living with T1D included in Société Francophone du Diabète – Cohorte Diabète de Type 1 (SFDT1), a French longitudinal cohort study. HRQoL was assessed using generic (EuroQol 5‐Dimensions 5‐Level questionnaire [EQ‐5D‐5L]) and diabetes‐specific (Audit of Diabetes‐Dependent Quality of Life) instruments. The severity of diabetes complications was measured using an adapted Diabetes Complication Score Index (DCSI) ranging from 0 to 14. We used multiple imputations to deal with missing data. Results We included 1892 adults, 48% women, with a median (interquartile range [IQR]) age of 38 (28; 51) years. The mean overall EQ‐5D‐5L HRQoL score was 71.1 ± 17.7 (maximum 100), with the following number of participants negatively impacted for each domain: 271 (14%) for mobility, 94 (5%) for self‐care, 378 (19%) for usual activities, 853 (45%) for pain/discomfort and 983 (52%) for anxiety/depression. The median (IQR) DCSI was 1 (0; 2). In multivariable models, a one‐step increase in DCSI was associated with a 1.5% decrease in overall EQ‐5D‐5L HRQoL. DCSI was also inversely associated with all domains of the generic scale except anxiety/depression and 17 domains of the diabetes‐specific scale. Conclusions We observed an inverse association between the severity of complications and overall HRQoL and most of its dimensions. Our results highlight the need to reinforce the prevention of complications to improve the overall well‐being of people with T1D
Eco-friendly Conductive biopolymer nanocomposites and Life Cycle Assessment: a review
No full textInternational audienceConductive bionanocomposites are attracting growing interest as multifunctional materials. They can meet the requirements of electrical applications while supporting sustainable development. This review summarizes recent research on bionanocomposites made from biopolymer matrices and carbon conductive fillers that can be processed by additive manufacturing. These materials offer several advantages, including reduced dependence on fossil resources, possibility of low-impact processing, minimized risks in case of dissemination, and satisfactory electrical properties with low amounts of conductive fillers. However, despite their “green' label, their actual environmental performance has not been fully demonstrated. Only a limited number of comprehensive Life Cycle Assessments (LCA) are available. This review discusses the potential of these materials, while underscoring the necessity for rigorous environmental analysis. Such assessments are essential to validate their sustainability from a circular economy perspective using LCA
Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes through FIB-SEM Characterization, Manufacturing and Electrochemical Modeling
No full textInternational audienceGiven the recent inclusion of sodium-ion batteries (SIBs) in the energy market, the optimization of their performance becomes a relevant research topic. At the electrode-level, the parameters selected during its manufacturing process influence its microstructure and, consequently, its electrochemical performance. Here, we address different manufacturing conditions of hard carbon (HC) negative electrodes by varying the solid content (35 wt% and 40 wt%) and the calendering degree (uncalendered and 30% calendered). The three-dimensional microstructure of each sample is acquired using focused ion beam (FIB) and scanning electron microscopy (SEM) technique, from which the real-shape of HC particles is extracted and used to generate input microstructures for a discrete element method (DEM) calendering model designed to address the mechanical electrode behavior and its effects on current collector deformation. Also, the 3D electrode microstructures are used in a finite element method (FEM) model to obtain the electrochemical performance for C-rates ranging from C/50 to C/5 and to compare these results with experimental ones. Furthermore, the DEM predictions are injected into the FEM model to validate them against the FIB-SEM reference. Overall, we study how manufacturing parameters influence the performance of HC electrodes, providing an important guideline for optimizing their production for SIBs applications. Preprint on ChemRxiv, 45 pages
Synchronous Miocene radiations and geographic-dependent diversification of pantropical Xylopia (Annonaceae).
No full textInternational audienc
A HyperFlash and ECLAT view of the local environment and energetics of the repeating FRB 20240619D
No full textInternational audienceTime-variable propagation effects provide a window into the local plasma environments of repeating fast radio burst (FRB) sources. Here we report high-cadence observations of FRB 20240619D, as part of the HyperFlash and ÉCLAT programs. We observed for h and detected bursts, including bursts with high fluence ( Jy ms) and implied energy. We track burst-to-burst variations in dispersion measure (DM) and rotation measure (RM), from which we constrain the parallel magnetic field strength in the source's local environment: mG. Apparent DM variations between sub-bursts in a single bright event are interpreted as coming from plasma lensing or variable emission height. We also identify two distinct scintillation screens along the line of sight, one associated with the Milky Way and the other likely located in the FRB's host galaxy or local environment. Together, these (time-variable) propagation effects reveal that FRB 20240619D is embedded in a dense, turbulent and highly magnetised plasma. The source's environment is more dynamic than that measured for many other (repeating) FRB sources, but less extreme compared to several repeaters that are associated with a compact, persistent radio source. FRB 20240619D's cumulative burst fluence distribution shows a power-law break, with a flat tail at high energies. Along with previous studies, this emphasises a common feature in the burst energy distribution of hyperactive repeaters. Using the break in the burst fluence distribution, we estimate a source redshift of -. We discuss FRB 20240619D's nature in the context of similar studies of other repeating FRBs
La recherche partenariale : une alliance stratégique entre recherche fondamentale et monde industrie
No full textLa Fabrique de l'IndustrieDans une économie fondée sur la connaissance, les collaborations entre universités et entreprises jouent un rôle central dans l’innovation, la croissance économique et la transformation des sociétés. Loin de se limiter à un simple transfert de technologies ou à une sous-traitance de compétences scientifiques, ces partenariats sont aujourd’hui des espaces structurants de coconstruction des savoirs, où se redéfinit la frontière entre recherche académique et monde industriel
AI-Driven Multi-Agent System for Autonomous Mining Operation Centers
No full textInternational audienceAS mining operations enter the era of Industry 4.0, traditional Remote Operation Centers remain constrained by reactive workflows and limited automation. This work introduces EI2ROC, a next-generation agentic AI framework that transforms mining IROCs into proactive, semi-autonomous decision engines. By orchestrating specialized agents for operational intelligence (VDT, OEE, SIC) and deep-learning-based production forecasting, EI2ROC enables continuous diagnostic insight, risk-aware capacity prediction, and automated corrective actions. Early results indicate a potential 70-80% reduction in manual monitoring and significant performance gains through anticipatory control. Designed as a modular, scalable architecture, EI2ROC offers a transferable blueprint for autonomous, data-driven operations across mining and other asset-intensive Industry 4.0 domains.À mesure que les opérations minières entrent dans l'ère de l'Industrie 4.0, les Centres d'Opération à Distance traditionnels restent contraints par des flux de travail réactifs et une automatisation limitée. Ce travail présente EI2ROC, un cadre d'IA agentique de nouvelle génération qui transforme les IROCs miniers en moteurs de décision proactifs et semi-autonomes. En orchestrant des agents spécialisés pour l'intelligence opérationnelle (VDT, OEE, SIC) et la prévision de production par apprentissage profond, EI2ROC permet un diagnostic continu, une prédiction de capacité tenant compte des risques, et des actions correctives automatisées. Les premiers résultats indiquent une réduction potentielle de 70 à 80 % de la surveillance manuelle et des gains de performance significatifs grâce au contrôle anticipatif. Conçu comme une architecture modulaire et évolutive, EI2ROC offre un modèle transférable pour des opérations autonomes et pilotées par les données, applicable au secteur minier et à d'autres domaines industriels à forte intensité d'actifs relevant de l'Industrie 4.0
Trivalence and Transparency: a non-dynamic approach to anaphora
No full textThis paper offers a new theory of donkey anaphora that does not include any dynamic component. Even if the approach is not dynamic, it retains a key aspect of the dynamic tradition, namely the view that information states include not just factual information about the world, but also information about discourse referents, e.g., variables. It also makes crucial use of plural assignment functions (sets of standard assignments, cf. van der Berg 1996; Nouwen 2003; Brasoveanu 2008). Unlike dynamic approaches, sentences are evaluated as true or false relative to a pair (w, G), where w is a possible world and G is a plural assignment, with no reference to contexts or information states, and compositional semantics does not refer in any way to context update. In order to predict adequate meanings and felicity conditions, I combine two ingredients that have been used to account for presupposition projection, namely Trivalence (Peters 1979; Beaver and Krahmer 2001) and Schlenker’s Transparency Principle (Schlenker 2007, 2008a). Two ideas play a crucial role in the proposal. First, a sentence such as ‘Shex came’ comes with the presupposition that the variable x is ‘valued’ and denotes an atomic individual, which means that every atomic assignment in G maps x to the same atomic value. Second I adopt Mandelkern’s (2022) witness condition: an existential statement such as ‘Someonex came’ is undefined in (w, G) if it is classically true in w but G does not map x to a witness of the existential statement. Importantly, undefinedness is not equated with Presupposition Failure (e.g., even though ‘Someonex came’ can be undefined, it is in fact never a presupposition failure). Rather, presupposition projection is governed by Schlenker’s Transparency Principle (Schlenker 2007, 2008a): the presupposition ‘x is valued and atomic’ should be redundant in the syntactic position in which ‘Shex came’ occurs. In the end of the paper, I discuss well-known ambiguities with donkey sentences (weak vs. strong, existential vs. universal readings) and show how they can be addressed in my system. The theory is presented here as a non-standard semantics for first-order logic, rather than a fragment of a natural language. Free variables are the counterparts of syntactically unbound pronouns, and existential quantifiers those of singular indefinites
Simultaneous Misalignment and Mode Mismatch Sensing in Optical Cavities Using Intensity-Only Measurements
No full textInternational audiencePrecise sensing and control of spatial mode content is essential for the performance of precision optical systems, particularly interferometric gravitational-wave detectors, where misalignment and mode mismatch can lead to significant optical losses and degraded quantum noise suppression. Conventional approaches, including heterodyne wavefront sensing and phase camera techniques, are effective but can be limited by hardware complexity and systematic uncertainties arising from restricted reference-beam overlap. This paper presents a novel two-step deep learning pipeline for robust beam diagnostics based solely on beam intensity images. In the first stage, a multi-intensity-image convolutional neural network (CNN) performs accurate mode decomposition, recovering the complex modal content of distorted beams. In the second stage, the predicted mode coefficients are fed into a downstream regression network that simultaneously estimates all eight degrees of freedom (DoFs) associated with misalignment and mode mismatch, including beam tilt, lateral offset, and waist size and position mismatches in both transverse directions. The proposed CNN-based framework achieves a mean absolute error (MAE) of 0.0034 in the mode decomposition stage, which propagates to a total MAE of 0.0062 in the recovered beam imperfection parameters at the final stage. This corresponds to an average residual optical loss of 39 ppm per DoF (310 ppm total). This approach relies only on standard CCD imaging and is robust to random intensity noise, eliminating the need for complex interferometric hardware. The results demonstrate that the proposed deep learning pipeline enables real-time, high-accuracy wavefront sensing and mode-mismatch diagnostics, providing a scalable and hardware-efficient tool for improving the stability and sensitivity of precision optical systems
Comment je fais… un cerclage cervical définitif par voie laparoscopique
No full textInternational audienceNo abstract availabl